上海国土资源2025，Vol.46Issue(3)：68-78,11.DOI:10.3969/j.issn.2095-1329.2025.03.010
深基坑降水函数的构建与智能实现
Construction and intelligent implementation of deep excavation dewatering function
摘要
Abstract
In order to enhance the scientific and intelligent level of deep foundation pit dewatering design,a method for constructing a foundation pit dewatering function by combining numerical simulation and machine learning was proposed taking Shanghai as the research background.Based on the typical groundwater-land subsidence dual control zoning of foundation pit dewatering in Shanghai area,a three-dimensional groundwater numerical model was established for partitions ⑦Ⅱ1-1 and ⑦Ⅱ2-3.The influence of factors,including foundation pit area,aspect ratio,excavation depth,and curtain penetration depth into the aquifer on the groundwater drawdown at distances of 0.5H,1H,2H,and 3H(H is the excavation depth of the foundation pit)outside the pit were analysed.On this basis,multiple linear regression method was used to construct explicit functional relationships,and BP and MLP neural network models were utilized to improve the accuracy and applicability of drawdown prediction.The research results indicate that neural network models have good fitting ability for nonlinear relationships between complex variables,and the predicted values are highly consistent with the measured data,which can achieve rapid and intelligent prediction of groundwater drawdown at various monitoring points outside the foundation pit.The research provides new methods and technical support for optimizing deep foundation pit dewatering schemes in complex urban areas.关键词
深基坑/降水预测/数值模拟/BP神经网络/MLP模型Key words
deep foundation pit/dewatering prediction/numerical simulation/BP neural network/MLP neural network分类
土木建筑引用本文复制引用
王建秀,龙燕霞,王禹,杨天亮,殷立峰,晏殊,林子越..深基坑降水函数的构建与智能实现[J].上海国土资源,2025,46(3):68-78,11.基金项目
上海市住房和城乡管理委员会重点课题(2024-Z02-007) （2024-Z02-007）
上海长凯岩土工程有限公司课题(kh0023020231733) （kh0023020231733）
上海市交通委员会科研项目(JT2025-KY-027) （JT2025-KY-027）
上海申通地铁集团有限公司科研项目(JS-KY24R006) （JS-KY24R006）
